Mobile Robot Control 2023 Ultron: Difference between revisions
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==Introduction== | ==Introduction== | ||
In an increasingly autonomous world, robotics plays a vital role in performing complex tasks. One such task is enabling a robot to autonomously locate and navigate itself in a restaurant environment to serve customers. While this may seem like a straightforward task for humans, there are various challenges when considering robots. | In an increasingly autonomous world, robotics plays a vital role in performing complex tasks. One such task is enabling a robot to autonomously locate and navigate itself in a restaurant environment to serve customers. While this may seem like a straightforward task for humans, there are various challenges when considering robots. | ||
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Another difficulty is localization, which is required for an autonomous robot to determine its precise position and orientation. Particle filtering and Kalman filtering are two techniques that can be used to combine sensor measurements with a predetermined map, compensating for imperfections and adapting to real-world scenarios. | Another difficulty is localization, which is required for an autonomous robot to determine its precise position and orientation. Particle filtering and Kalman filtering are two techniques that can be used to combine sensor measurements with a predetermined map, compensating for imperfections and adapting to real-world scenarios. | ||
Combining localization and navigation techniques is the final challenge. A robot can identify its location on a map, plan an optimal path, and successfully complete complex tasks by maneuvering precisely to the designated table by developing the necessary software. | Combining localization and navigation techniques is the final challenge. A robot can identify its location on a map, plan an optimal path, and successfully complete complex tasks by maneuvering precisely to the designated table by developing the necessary software. | ||
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==The challenge and deliverables== | ==The challenge and deliverables== | ||
The basic map for the challenge contains the tables and their numbers correspondingly, walls and doors. During the challenge, the robot starts at an area of 1x1 meters in an arbitrary orientation. The robot then has to make its way to each of the tables, orient itself and announce that it has arrived at the respective table before moving to the next table in the sequence. To make things more challenging, a few unknown static and dynamic obstacles were also added to the map. The presence of these obstacles is not known to the robot prior to the commencement of the challenge. And it has to safely navigate around the obstacles without bumping into any of them. | The basic map for the challenge contains the tables and their numbers correspondingly, walls and doors. During the challenge, the robot starts at an area of 1x1 meters in an arbitrary orientation. The robot then has to make its way to each of the tables, orient itself and announce that it has arrived at the respective table before moving to the next table in the sequence. To make things more challenging, a few unknown static and dynamic obstacles were also added to the map. The presence of these obstacles is not known to the robot prior to the commencement of the challenge. And it has to safely navigate around the obstacles without bumping into any of them. |
Revision as of 21:56, 3 July 2023
Group members:
Name | student ID |
---|---|
Sarthak Shirke | 1658581 |
Idan Grady | 1912976 |
Ram Balaji Ramachandran | 1896067 |
Anagha Nadig | 1830961 |
Dharshan Bashkaran Latha | 1868950 |
Nisha Grace Joy | 1810502 |
Design Presentation Link
Introduction
In an increasingly autonomous world, robotics plays a vital role in performing complex tasks. One such task is enabling a robot to autonomously locate and navigate itself in a restaurant environment to serve customers. While this may seem like a straightforward task for humans, there are various challenges when considering robots. Planning the best routes for the robot to follow in order to navigate is one of the main challenges. Global path planning algorithms like A* or Dijkstra's algorithm, which guarantee effective route planning based on a predefined map, are just two examples of techniques that can be used to address this issue.
Techniques like the artificial potential field method or the dynamic window approach can be used for local navigation to achieve real-time obstacle avoidance and adaptation to dynamic objects, addressing the challenges posed by unknown and dynamic scenes.
Another difficulty is localization, which is required for an autonomous robot to determine its precise position and orientation. Particle filtering and Kalman filtering are two techniques that can be used to combine sensor measurements with a predetermined map, compensating for imperfections and adapting to real-world scenarios. Combining localization and navigation techniques is the final challenge. A robot can identify its location on a map, plan an optimal path, and successfully complete complex tasks by maneuvering precisely to the designated table by developing the necessary software.
The challenge and deliverables
The basic map for the challenge contains the tables and their numbers correspondingly, walls and doors. During the challenge, the robot starts at an area of 1x1 meters in an arbitrary orientation. The robot then has to make its way to each of the tables, orient itself and announce that it has arrived at the respective table before moving to the next table in the sequence. To make things more challenging, a few unknown static and dynamic obstacles were also added to the map. The presence of these obstacles is not known to the robot prior to the commencement of the challenge. And it has to safely navigate around the obstacles without bumping into any of them.
Game plan and approach
Milestones and achievements
Simulator vs Real world
Discussion and future scope
//On what we saw and why
Conclusion